class_docstr: RawNet3 encoder. Extracts frame-level RawNet embeddings from raw waveform.
  paper: J. Jung et al., "Pushing the limits of raw waveform speaker
      recognition", in Proc. INTERSPEECH, 2022.
  
  Args:
      input_size: input feature dimension.
      block: type of encoder block class to use.
      model_scale: scale value of the Res2Net architecture.
      ndim: dimensionality of the hidden representation.
      output_size: ouptut embedding dimension.
code: def __init__(
      self,
      input_size: int,
      block: str = "Bottle2neck",
      model_scale: int = 8,
      ndim: int = 1024,
      output_size: int = 1536,
      **kwargs,
  ):
      assert check_argument_types()
      super().__init__()
      if block == "Bottle2neck":
          block = Bottle2neck
      else:
          raise ValueError(f"unsupported block, got: {block}")
  
      self._output_size = output_size
  
      self.relu = nn.ReLU()
  
      self.layer1 = block(
          input_size,
          ndim,
          kernel_size=3,
          dilation=2,
          scale=model_scale,
          pool=5,
      )
      self.layer2 = block(
          ndim,
          ndim,
          kernel_size=3,
          dilation=3,
          scale=model_scale,
          pool=3,
      )
      self.layer3 = block(
          ndim, ndim, kernel_size=3, dilation=4, scale=model_scale
      )
      self.layer4 = nn.Conv1d(3 * ndim, output_size, kernel_size=1)
  
      self.mp3 = nn.MaxPool1d(3)
  
file_path: espnet/espnet2/spk/encoder/rawnet3_encoder.py
imports: import torch
  import torch.nn
  from typeguard import check_argument_types
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  from espnet2.spk.layers.rawnet_block import Bottle2neck
label_desc: Use image node_id 1 to create a new RawNet3Encoder object from inherited base classes: AbsEncoder with example: obj = RawNet3Encoder(input_size, block, model_scale, ndim, output_size)